Structure based 3D-QSAR studies of Interleukin-2 inhibitors: Comparing the quality and predictivity of 3D-QSAR models obtained from different alignment methods and charge calculations

Interleukin-2 is an essential cytokine in an innate immune response, and is a promising drug target for several immunological disorders. In the present study, structure-based 3D-QSAR modeling was carried out via Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) methods. Six different partial charge calculation methods were used in combination with two different alignment methods to scrutinize their effects on the predictive power of 3D-QSAR models. The best CoMFA and CoMSIA models were obtained with the AM1 charges when used with co-conformer based substructure alignment (CCBSA) method. The obtained models posses excellent correlation coefficient value and also exhibited good predictive power (for CoMFA: q(2) = 0.619; r(2) = 0.890; r(Pred)(2) = 0.765 and for CoMSIA: q(2) = 0.607; r(2) = 0.884; r(Pred)(2) = 0.655). The developed models were further validated by using a set of another sixteen compounds as external test set 2 and both models showed strong predictive power with r(Pred)(2) = >0.8. The contour maps obtained from these models better interpret the structure activity relationship; hence the developed models would help to design and optimize more potent IL-2 inhibitors. The results might have implications for rational design of specific anti-inflammatory compounds with improved affinity and selectivity. (C) 2015 Elsevier Ireland Ltd. All rights reserved.